Human attention is a finite resource. When interrupted while performing a task, this resource is split between two interactive tasks. People have to decide whether the benefits from the interruptive interaction will be enough to offset the loss of attention from the original task. They may choose to ignore or delay dealing with the interruption to a more convenient time. Alternatively, they may choose to immediately address the interruption but this comes with a risk of reduced performance on the primary task or a delay in resuming their primary task.
The issue of dealing with peripheral tasks (both self-interruptions and external interruptions) is particularly critical in driving situations. Under normal driving conditions, drivers should have an appropriate following distance (ideally 2-3 seconds behind the car in front) for safe driving. This following distance provides the driver with enough reaction time to decide whether to stop, slow down, or otherwise react to changing driving conditions.
However, driving guidelines like this assume that the driver is fully attending to the driving task. If a driver is performing peripheral tasks, those not directly related to the driving task (e.g., turning off a smartphone alarm or changing the radio station), the driver needs to manage if and when to divide his or her attention. Depending on the frequency and duration of these additional tasks, drivers need to adapt how they apply safe driving guidelines.
Fortunately, drivers naturally adapt to changing driving conditions when dealing with peripheral tasks, e.g., waiting for a red light to attend to these tasks, and not performing them in heavy traffic. They determine appropriate timings for changing the positions of their hands on the steering wheel, controlling the foot pedals, all while monitoring adjacent traffic. However, if a demand for peripheral interaction arrives at an inappropriate or unexpected time (e.g., phone rings while changing lanes to exit the highway), it can lead to dangerous driving situations which may result in a driving violation, accident or even loss of life. In fact, 25% of car accidents in the U.S. are related to phone use.
By identifying situations or patterns when drivers are not able to attend to peripheral tasks due to their current state and driving situation, a workload manager may regulate the flow of information to drivers that could otherwise interfere with driving. Intelligent systems will be able to mediate the delivery of external interruptions, or even disable phone/infotainment systems to mediate self-interruptions, to support safer driving.
Traditionally, user experience has been sampled by asking people to stop mid-task and note their experience. The point is for users to record in situ aspects of experience like mental effort or emotion, based on their own judgment. When users are engaged in naturalistic and uncontrolled real-world tasks, that approach provides low-resolution data since sampling rates need to be low to avoid disrupting the user too much, but are often too low to track dynamically varying user states. This problem is especially disadvantageous in mobile contexts such as driving.
In automotive contexts, when directed to self-report experience, drivers must divert their attention from the driving task. This can diminish cognitive capabilities for the primary task by drawing attention to interruptive demands for peripheral interaction; while driving, interruptions can negatively impact primary task performance. When sampling user state at the end of a driving session, in situ variations of that state can be blurred in relation to the overall user experience. Due to these potential issues, driver experience sampling based on self-reports has typically been explored for evaluating user interfaces (e.g., dashboard designs) or within driving simulations where interruptions are less dangerous.
Interruptions
As the physical world becomes increasingly connected with our information spaces, so too is the likelihood that information will be pushed to people during the performance of real-world tasks. At best, those interruptions may alert users to important warnings or messages, inquire about people's status (e.g., affective states or health conditions for health care), or deliver information that can benefit task performance. Despite this potential value, dealing with these interruptions through peripheral interaction (interaction not directly related to the primary task) demands cognitive attention that can negatively and variably impact user experience. An improved understanding of user availability or interruptibility is necessary for mediating this impact.
Task interruptions result in a time lag before users can resume their primary task, and thus decrease primary task performance. Appropriate timings of interruptions can reduce the impact on users. For example, in the context of desktop computing, interruptions delivered at points of lower mental workload reduced resumption lags and minimized disruption in primary task performance compared to interruptions at points of higher mental workload. In an experiment in which participants were interrupted with emails about consumer products and prices, users who experienced deferrable interruptions during high cognitive workload tasks frequently disregarded the notifications until they reached low workload periods. The results of another experiment showed that when peripheral tasks involving reasoning, reading or arithmetic interrupt the execution of primary tasks, users require more time to complete the primary tasks, commit more errors, and become more annoyed and increasingly anxious than when those same peripheral tasks were presented at the boundary between primary tasks.
These studies offer important insights for designing human-centered interruptions; however, they have mostly explored static, on-screen tasks mediated with conventional computers or mobile devices (e.g., the impact of call notifications for smartphone users). Little research has been conducted to replicate these findings or approaches for delivering interruptions in situations in which users cannot fully divert their attention from the primary task (e.g., driving cars) and in which interruption timings can critically impact the user experience.
Naturalistic Driving
To ensure driver safety, driving studies about dual task paradigms have mostly been conducted in simulated environments. When using sensors to track drivers' eye gazes or physiological responses in these simulations, the experimental design requires special attention to achieve valid and realistic data. Due to the issue of driver safety, existing naturalistic driving datasets mostly include audiovisual records, traffic and lane information from vision-based systems, and researcher-estimated driver states using image processing techniques on videos from multiple cameras installed in cars.
Recent advances in wearable technologies have resulted in sensors that are less intrusive and more comfortable to wear. Nevertheless, little research has been performed that tracks driver body motion or physiological responses during naturalistic driving. Driver work load has been mostly evoked by imposing artificial dual-task demands, for example, auditory stimuli at pre-structured interaction times and intervals. On-board diagnostics (OBD) systems or accelerometers on the steering wheel have been used to assess driver aggressiveness, driving environment and vehicle states.